Following examination of the numerous options for data analysis (Ritchie et al. 2013; De Vaus and de Vaus 2001; Miles and Huberman 1994; Patton 1990; Weber 1990) directed content analysis was chosen as the preferred method. Content analysis, in both its basic form and as directed content analysis, fits well with a critical theory research philosophy in that ‘content analysis is a flexible research method for analysing texts and describing and interpreting the written artefacts of a society’ (White and Marsh 2006 in Beach et al. 2009 p.129). This is particularly appropriate to this study due to the proliferation of different ‘artefacts’ collected from Twitter, blogs, and other electronic sources and also the traditional press and transcriptions of the research interviews. Similarly, Weber (1990 p.5), in a way that again matches the types of data collected in this project, suggests that ‘social scientists who must make sense of historical documents, newspaper stories, political speeches, open-ended interviews…to name a few – will find the technique indispensable’. Content analysis has also been described as having advantages in preference to other analytical approaches as ‘it allows a closeness to text which can alternate between specific categories and relationships’
(CSU 2018). This again corresponds with this study in the way that it is focused explicitly upon the categorisation of data in relation to a number of different strands and models of political communication. Another attributed benefit of content analysis, which also tallies with the approach of this study, particularly concerning initial data collected from Twitter, is the manner in which content analysis is suggested to be particularly suited to unobtrusive research methods (ibid).
Directed content analysis ‘is guided by a more structured process than in a conventional content analysis approach. Using existing theory or prior research, researchers begin by
Chapter 5 – Methodology 99 identifying key concepts or variables as initial coding categories’ (Hsieh and Shannon 2005 p.1281). This is appropriate to this study as part of its central aim is to test existing strands/models of democratic communication against the data collected during the #indyref.
The coding categories to the primary research question are discussed in detail in the following sections. This was initially done by formalising data categories in line with an abductive reasoning approach, whereby the pertinent themes were identified through analysis of the literature included in the theory section of the thesis and also from the research interviews.
This approach can be classed as abductive as the themes identified did not guarantee any conclusions, as would be the case with a deductive reasoning approach. The research approach, for instance, took the form of broader categories of data relating to deliberation and agonism and data which could be appropriated to one or more of Kuhn’s five functions of the media (2007). These broader categories of data were then finally pared down at a later date into the sections and indicative units included in Freelon’s (2010) model of online democratic communication (these will be discussed in section 5.13).
Nvivo software was used to collate and analyse data. Nvivo software is specifically designed for qualitative research and can be used to store, analyse, and model data collected across different mediums. Nvivo was also used in the transcription of all the interview recordings, and this was done by uploading interview recordings on to the database and then manually typing the recordings as the audio was played back. Although this technique takes some time, it facilitates the researcher to become immersed in the data and also allowed specific highlights in the data to be appropriately tagged and easily re-engaged with at the appropriate time in the analysis process.
Led by the literature review in the earlier parts of the study, it was decided that the empirical project should focus upon the consideration of both deliberative and agonistic theory during the independence referendum campaign. This was an organic process as it quickly became apparent that there was a paucity of any kind of genuine deliberation during proceedings on Twitter. The more conflictual nature of overall proceedings resonated better with agonistic theory and particularly that of Chantal Mouffe in On the Political (2005). This was later confirmed during the data allocation process relating to Freelon’s (2010) model which was limited to three approaches, namely – liberal individualist, communitarian, and deliberative. At this stage, all the existing data from Twitter, the traditional press, and the interviews was manually reallocated into groups relating to each of Freelon’s units of analysis (see section 5.13). Though the data fitted into these categories to one extent or another, the lack of an agonistic strand of the model seemed inappropriate. Such a combined approach of
Chapter 5 – Methodology 100 deliberation and agonism has been proposed by others in the field such as Karppinen, Moe and Svensson (2008) who suggest:
Consensus and conflict are two co-existing impulses of political communication and political life in general…not disregarding differences [between consensus and agonism] we argue that an openness to potential combinations of the two approaches might have positive implications for media and communication research (Karppinen, Moe and Svensson 2008 p.11).
In order to support this analytical strategy, it was important to produce a robust methodology, and as already touched upon, this was derived from the work of Deen G. Freelon in Analyzing online political discussion using three models of democratic communication (2010) (again, see the following section).
The supplementary research question explores the relevance of pluralist group theory in relation to Twitter use during the Scottish independence referendum 2014. This question stands separate to the primary research question, as detailed earlier in this chapter, with regard to data analysis. Initially, the method of analysis for this question was to simply add another unit of analysis in addition to those in Freelon’s model and assess the data related to theoretical debates around group theory, particularly those identified in Dahl’s seminal work in the area – Who Governs? (2005). Further detailed analysis resulted in a more specific coding scheme taken from Dahl and Lindblom’s Politics Economics and Welfare (1976), where the authors identify five distinct political effects of social/interest groups:
1. They are more effective than individuals 2. They facilitate healthy political competition
3. The group bargaining process creates a barrier to extremism
4. Overlapping memberships of social groups discourages unilateral thought and action 5. Extensive pluralist networks help to ensure the spread of information (Dahl and
Lindblom (1976 p.302-305)
This maintained a very specific focus on applying data to theoretical work in the subject field in the same way that the coding and analysis relating to the primary research question had with the use of Freelon’s (2010) model. In addition to this, one further category was added to the process regarding group/state personality, as generally appropriated to the works of the English pluralists such as Laski, Cole, and Figgis (see chapter 3). This was through a desire
Chapter 5 – Methodology 101 to assess if the phenomenon of social media had changed classical conceptions of the relationship between the state and its citizens.